# Provably Efficient Sensor Allocation for Unknown High-dimensional Systems with Limited Sensing

arXiv: https://arxiv.org/abs/2605.16584 · HTML: https://mlsanity.com/paper/2605.16584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2026.

## Built on by (most cited first, 1 of 1)

- [Learning structured linear dynamical systems from missing observations](https://mlsanity.com/paper/2610.11869/) (2026)

## Abstract (arXiv)

This paper focuses on learning efficient sensor allocations that ensure observability of unknown high-dimensional linear systems using only a small number of sensors. Existing methods either require an impractically large number of sensors or assume access to an observable allocation in advance. We propose a two-stage framework that overcomes these limitations: first, a novel system identification algorithm integrates information from multiple trajectories, each observing different subsets of state coordinates; then, a classic sensor allocation method is adapted to operate on the learned system parameters. Our non-asymptotic guarantees show that the proposed approach learns a sensor allocation with a near-optimal number of sensors when sensors can be allocated on any state coordinate. We further extend the results to settings with inaccessible state coordinates that are unavailable for sensor allocation.